We also provide examples where we reconstructed a very sparse set of 3D points (32 points) from two images and utilized those as the 3D inputs. The dense depth maps obtained with this setting using our method, NLSPN [1]} and MVSNet [2]} are illustrated in Figure REF . We argue that state-of-the-art depth completion methods are usually vulnerable in high sparsity cases, while deep multi-view stereo performance degrades with less input views. One the other hand, our method produces high quality depth maps with significantly less distortions. Additional results are provided in the supplementary material.
